Modified-LRU Algorithm for Caching on Named Data Network

Kurniawan, Fandi Setio; Yovita, Leanna Vidya; Wibowo, Tody Ariefianto

doi:10.1109/iceei47359.2019.8988836

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…In computing, FIFO, 29 LRU, 30 LFU, 31 RR, 32 and SF‐LRU 33 are five cache replacement algorithms that manage a cache of information stored on a computer. The FIFO algorithm replaces the cache of data according to the data first entering the cache 29 .…”

Section: Related Workmentioning

confidence: 99%

“…The RR algorithm replaces the cache of data according to the random replacement data principle 32 . The SF‐LRU algorithm combines the LRU and LFU using the second chance concept 33 . The improved versions of the LRU and LFU algorithms are suitable for network cache applications such as content delivery networks and information‐centric networking.…”

Section: Related Workmentioning

confidence: 99%

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Value‐aware cache replacement in edge networks for Internet of Things

Peng

Wang

Liang

et al. 2021

Trans Emerging Tel Tech

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With the development of Internet of Things (IoT), massive amounts of data will be brought. By offloading caching from the cloud to the edge, edge caching technology represents a promising solution in the era of IoT to meet the delay requirements of IoT applications. An efficient cache decision and replacement strategy on edge caching devices is a key factor in ensuring the cache hit ratio. The existing cache replacement policies do not comprehensively consider the characteristics of cache files and are likely to result in cache pollution problems. In order to cache data reasonably, to improve the cache hit ratio, and to reduce the user request delay, we propose a concept of file cache value and a file cache value‐aware cache replacement (FCVACR) algorithm of the edge cache system in this article. File cache value is associated with three aspects of cache file: the file size, the file popularity, and the time of requests. The proposed FCVACR algorithm adopts the file cache value method, improves the utilization of edge caching space, and reduces the content transmission delay. Experimental results show that the proposed FCVACR algorithm has a higher cache hit ratio and lower user request delay than the classical cache replacement algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Value‐aware cache replacement in edge networks for Internet of Things

Peng

Wang

Liang

et al. 2021

Trans Emerging Tel Tech

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show abstract

“…In this paper, we use MATLAB tools for simulation analysis. Based on the proposed edge network architecture and collaborative caching scheme of SGIN, first, we solve the value of L and F. Second, the benefits of the content update algorithm LF are verified by comparing with the traditional algorithms least recently used (LRU) [44] and least frequently used (LFU) [45]. Second, we determine the best way to deploy edge cache nodes under different traffic conditions in different regions and verify the benefits of deploying hierarchical cache nodes.…”

Section: Simulation Analysismentioning

confidence: 99%

A Collaborative Cache Strategy in Satellite‐Ground Integrated Network Based on Multiaccess Edge Computing

Yang

Kong

et al. 2021

Wireless Communications and Mobile Computing

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Multiaccess edge computing (MEC) provides users with a network environment and computing storage capacity at the edge of the network, ensuring a deterministic service with low delivery delay. This paper introduces a new satellite-ground integrated collaborative caching network architecture based on MEC and studies the caching strategy. On the ground side, the edge nodes (ENs) are deployed to the user side to form a hierarchical collaborative cache mode centered on the base station. On the satellite side, we utilize intelligent satellite ENs to precache and multicast the highly popular contents, reducing the initial content delivery delay. Under the constraints of the user demand and storage capacity, we study the deployment and cache scheme of ENs and establish the delivery delay minimization problem. To solve the problem, we propose a content update decision parameter for content cache update and transform the problem into improving the hit rate of ENs. Simulation results show that the proposed MEC network architecture and content caching scheme can increase the caching system hit rate to 64% and reduce the average delay by 32.96% at most.

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“…LFU [137] Hybrid Modified-LRU [81] LRFU [118] Content-based MDMR [66] Centrality-based NICE [75] Caching decision strategies Traditional CEE/LCE [73,163] LCD [162] Probabilistic Static Prob(p) [68,165] Dynamic BRR-CVR [42] MACD [50] pCASTING [68] CSCM [103] ProbCache [116] Vural et al [142] Topology/Centrality-based Betw/EgoBetw [41] BRR-CVR [42] Doan Van & Ai [50] PCS [100] CSCM [103] ProbCache [116] Shekhawat et al [132] Sun [135] Zeng & Hong [161] Zhang et al [164] LCC [170] BNC [172] Cooperative Explicit EM3C [76] Liu et al [92] PCS [100] Implicit…”

Section: Existing Studies Of Icn Caching In Iotmentioning

confidence: 99%

“…This will eventually result in higher server load, as caches accumulate stale items instead of storing fresher content, forcing more Interests to be routed to the original producers [137]. Modified-LRU [81] and LRFU [118] are novel hybrid approaches that consider both recency and frequency and as such should be less susceptible to frequency spikes.…”

Section: Least Recently Used (Lru)mentioning

confidence: 99%

Lightweight Caching and Load Balancing for Efficient Content Delivery in Information-Centric IoT

Pfender¹

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<p>In recent years, Information-Centric Networking (ICN) has emerged as a promising candidate for a future Internet architecture. While originally designed with the traditional Internet in mind, it has also been identified as a potential replacement for current Internet of Things (IoT) networking solutions. However, applications in the IoT face a number of unique challenges due to the constrained nature of the hardware. One of these challenges is that available memory is often extremely limited. This thesis aims to evaluate the feasibility of using ICN in-network caching on IoT devices in order to achieve efficient content delivery. It evaluates the performance of existing approaches on constrained hardware and explores how the technology can be improved and tailored towards that environment. Existing strategies are found to be lacking in key aspects, particularly the fact that the effects of network topology are often not considered when making caching decisions. It is shown that approaches based on network centrality are promising, but existing implementations are not suited for constrained hardware. Therefore, a lightweight in-network caching strategy called Approximate Betweenness Centrality (ABC) is proposed that takes the specific requirements of IoT into consideration and allows for efficient cache placement regardless of network topology. Then, a modular solution for load balancing through off-path caching is presented to address potential shortcomings of the centrality-based caching approach. It allows the network to make more efficient use of available caching resources without introducing additional overhead. Furthermore, solutions for ensuring Quality of Service (QoS) are discussed. The expanded role of caching strategies under such QoS constraints is explored and their performance is evaluated. This thesis shows that it is possible to design and deploy lightweight, low-overhead solutions on constrained hardware. Using a realistic deployment of physical IoT devices, it is demonstrated that these approaches can reach satisfactory levels of performance.</p>

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Modified-LRU Algorithm for Caching on Named Data Network

Cited by 8 publications

References 5 publications

Value‐aware cache replacement in edge networks for Internet of Things

Value‐aware cache replacement in edge networks for Internet of Things

A Collaborative Cache Strategy in Satellite‐Ground Integrated Network Based on Multiaccess Edge Computing

Lightweight Caching and Load Balancing for Efficient Content Delivery in Information-Centric IoT

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